Monte Carlo Modeling Services

Let FAS Solutions help your company meet the relative TSR award modeling challenges. We stand ready with unparalleled Monte Carlo technology to help our clients with these four sequential steps:

TSR Award Design

Pre-grant Valuation and Testing

ASC 718 Grant Date Valuation

Tracking and Forecasting

Our Monte Carlo modeling is the engine driving each of the first three steps as well as the forecasting aspect of step number four. In modeling TSR-awards conditional on relative TSR or absolute TSR, FAS Solutions work quality and credentials are unparalleled. This is a function of three factors:

Methodology – our valuations are the only ones in the equity compensation space that use cutting-edge Monte Carlo models employing full correlation matrix, antithetic variables and Mersenne twister random number generation in a 1-10 million simulation framework. The alternative of commonly used packaged software without full cross-correlations and the other refinements, as well as less simulations, typically results in higher valuations hitting the proxy as well as the P&L.

Access at the Top – your direct contact would be at the senior Ph.D. level. We “simplify the complex” and this is particularly true when it comes to our Monte Carlo modeling.

Relationships & Audit Experience – we have performed thousands of multi-factor Monte Carlo valuations for highly scrutinized clients that have passed audit without reservation at the big four among accounting firms nationwide. To date we have 100% acceptance of our work for over 300 client companies that depend on our Monte Carlo valuations. We have been told that our reputation at the accounting firms is unsurpassed; indeed auditors refer companies to us when aspects of the work done previously are problematic.

At FAS Solutions we use the Mersenne Twister MT 19937 pseudorandom number generator developed by Makoto Matsumoto and Takuji Nishimura (1998). We incorporate changes released by the Mersenne Twister inventors in 2002. The Mersenne Twister generates very high quality pseudorandom numbers and rectifies many of the flaws found in older algorithms. The random number generator produces uniformly distributed pseudorandom variables between 0 and 1. We then transform the uniformly distributed variables to normally distributed variables in generating each draw. Additionally we use the method of antithetic variates, which generates two paths at a time using the normal random variable and its negative. This method reduces variance by much more than a factor of two in Monte Carlo simulation.